iamrp.net-cNTi-1788861347-c835242802 1788861347 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2023 2 2 Daniel Brooks Amelia Carter Ethan Moore 2023 10.68159/c835242802 https://iamrp.net/pub/journal/1/article/c835242802 Li R, Xie L, Wang WY, Liaw PK, Zhang Y. High-Throughput Calculations for High-Entropy Alloys: A Brief Review. Front Mater. 2020;7:290. 10.3389/fmats.2020.00290. Marques F, Balcerzak M, Winkelmann F, Zepon G, Felderhoff M. Review and outlook on high-entropy alloys for hydrogen storage. Energy Environ Sci. 2021;14(10):5191-227. 10.1039/D1EE01543E. Oliveira TG, Fagundes DV, Capellato P, Sachs D, da Silva APAP. A Review of biomaterials based on high-entropy alloys. Metals. 2022;12(11):1940. 10.3390/met12111940. Liu C, Yang C, Liu J, Tang Y, Lin Z, Li L, et al. Medical high-entropy alloy: Outstanding mechanical properties and superb biological compatibility. Front Bioeng Biotechnol. 2022;10:952536. 10.3389/fbioe.2022.952536. Rao Z, Tung P-Y, Xie R, Wei Y, Zhang H, Ferrari A, et al. Machine learning–enabled high-entropy alloy discovery. Science. 2022;378(6615):78-85. 10.1126/science.abo4940. Fuhr AS, Sumpter BG. Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation. Front Mater. 2022;9:865270. 10.3389/fmats.2022.865270. Lyngby P, Thygesen KS. Data-driven discovery of 2D materials by deep generative models. npj Comput Mater. 2022;8(1):232. 10.1038/s41524-022-00923-3. Li Y, Hu J, Liu J, Yang Q, Zhang F, Cao H, et al. Inverse design with deep generative models: next step in materials discovery. Natl Sci Rev. 2022;9(8):nwac111. Mangos J, Birbilis N. Aluminium Alloy Design and Discovery using Machine Learning. arXiv preprint arXiv:2105.14806. 2021. Wang F, Zhang Y, Liaw PK, Li R, Xie L. Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for CO oxidation. Patterns (N Y). 2022;3(8):100548. 10.1016/j.patter.2022.100548. Grant J, Grant M, Olivares-Amaya R, Cordova M, Whelan J, Balasubramanian G, et al. Integrating atomistic simulations and machine learning to design multi-principal element alloys with improved resistance to hydrogen embrittlement. J Mater Sci. 2022;57:8108-26. 10.1007/s10853-022-07193-4. Wen C, Zhang Y, Wang C, Xue D, Bai Y, Antonov S, et al. Machine learning assisted design of high entropy alloys with low partial molar dilation for hydrogen storage. Acta Mater. 2020;196:84-93. 10.1016/j.actamat.2020.06.053. Bilodeau C, Rao GS, Stringer A, Duan C, Godin P, Radhakrishnan K, et al. A Generative Approach to Materials Discovery, Design, and Optimization. ACS Omega. 2022;7(28):23917-32. 10.1021/acsomega.2c03264. Ma Y, Wang Q, Gong H. A review on microstructures and properties of high entropy alloys manufactured by selective laser melting. J Alloys Compd. 2020;851:156755. 10.1016/j.jallcom.2020.156755. Gludovatz B, George EP, Ritchie RO. Fracture properties of high-entropy alloys. MRS Bull. 2022;47:129-36. 10.1557/s43577-021-00237-9. Zhang W, Liaw PK, Zhang Y. A brief review of high-entropy films. Mater Sci Eng A. 2021;804:140567. 10.1016/j.msea.2020.140567. Luo H, Li Z, Raabe D. Report on recent progress in the metallurgy, design, and manufacturing of high-entropy alloys for critical applications. J Mater Sci Technol. 2022;110:35-53. 10.1016/j.jmst.2021.08.045. Joseph J, Haghdadi N, Shamlaye K, Lamb O, Piazolo S, Molotnikov A, et al. An overview of high-entropy alloys as biomaterials. Metals. 2021;11:648. 10.3390/met11040648. Sathiyamoorthi P, Kim HS. High-entropy alloys with heterogeneous microstructure: Processing and mechanical properties. Prog Mater Sci. 2022;123:100709. 10.1016/j.pmatsci.2021.100709. Dan Y, Zhao Y, Li X, Li S, Hu S, Yang Y. Generative local metric learning for crystal materials discovery. Mater Today. 2020;41:19-28. 10.1016/j.mattod.2020.07.010. Kim G, Diao H, Hafner A, Howland E, Diao Y. Generative adversarial networks for crystal structure prediction. ACS Nano. 2020;14(12):17034-45. 10.1021/acsnano.0c07285. Gebauer NWA, Gastegger M, Schütt KT. Inverse design of 3d molecular structures with conditional generative neural networks. Nat Commun. 2022;13:973. 10.1038/s41467-022-28526-y. Court CJ, Cole JM. Auto-generated database of semiconductor band gaps using chemdataextractor. Sci Data. 2020;7:17. 10.1038/s41597-020-0359-0. Nouira W, Bengio Y, Jain A. Exploring deep generative models for materials discovery. Chem Mater. 2021;33(8):2854-65. Zhang L, Qian K, Schüller A, Thomy C. Review of Novel high-entropy protective materials: Wear, Irradiation, and Oxidation Resistance. Entropy. 2022;25(1):73. 10.3390/e25010073. Li W, Li Z, Wang Y, Liaw PK, Zhang Y. Mechanical behavior of high-entropy alloys. Prog Mater Sci. 2021;118:100777. 10.1016/j.pmatsci.2021.100777. Tsai KY, Tsai MH, Yeh JW. Sluggish diffusion in Co–Cr–Fe–Mn–Ni high-entropy alloys. Acta Mater. 2013;61(13):4887-97. Li Z, Körmann F, Grabowski B, Neugebauer J, Raabe D. Ab initio assisted design of quinary dual-phase high-entropy alloys with transformation-induced-plasticity. Acta Mater. 2017;136:262-70. Wang C, Ji Y, Wang Q, Gong H, Xu J. High-Entropy Coatings (HEC) for high-temperature applications: Materials, processing, and properties. coatings. 2022;12(5):691. 10.3390/coatings12050691. Senkov ON, Miracle DB. A new thermodynamic property model for multicomponent HCP solid solutions. J Alloys Compd. 2021;851:156920. 10.1016/j.jallcom.2020.156920. Miracle DB, Senkov ON. A critical review of high entropy alloys and related concepts. Acta Mater. 2017;122:448-511.